Image recognition has come a long way. At this point major imaging, storage, and social media vendors like Dropbox, Yahoo, Facebook, Google, Pinterest, and Shutterfly have all acquired image-recognition startups, and they are pursuing the holy grail of understanding what is shown in a consumer’s photo or video so that this imagery can be automatically categorized, retrieved, equipped with content-sensitive links, or otherwise leveraged.

Today’s consumers need the ability to locate relevant photos in their ever-expanding collections. Respondents in our GigaOm How apps can solve photo management survey assessed solutions for these needs to be valuable but mostly unavailable in the marketplace. While some image-recognition solutions cater to these consumer needs, others focus on the needs of advertisers and ecommerce vendors, who benefit from providing suggestions and links that are aware of image content, similar to how they have also leveraged the analysis of social media texts for advertising and sales purposes.

We believe the consumer-driven needs, coupled with the resources and motivation of the advertising-focused social media companies, will provide the image-recognition cross-market breakthroughs that requirements in asset management and stock photos, retail, health care, manufacturing and robotics, and security — or academia — have failed to deliver.

Those consumer offerings will build on academic deep-learning technology, which uses neural networks and massive computing power to create and refine image-recognition algorithms. Image recognition is not yet at the level of voice recognition or OCR and its accuracy varies widely, but given the fast progress, we expect it to get there for most use cases in the next 12 to 36 months. Non-consumer sectors should monitor and adopt the technologies driven by these innovations.